Data Science Manager, ML & Analytics Leadership

Capital One National Association; New York, Northern (NY, KY); Hybrid; USD 215,000 - 246,000; Full time

Job summary

Capital One is seeking a Data Scientist Manager to lead cross-functional teams across Capital One Data Insights and Analytics and to advance machine learning initiatives across billions of records. You will partner with data scientists, analysts, and relationship managers to deliver data-driven products and insights.

The role emphasizes building ML models through all stages, validating findings, and integrating new datasets, with a strong focus on scalable data infrastructure and enterprise data

Qualifications

• Bachelor's degree in a quantitative field plus 6 years of analytics experience or
• Master's degree in a quantitative field or MBA with quantitative concentration plus 4 years of analytics experience or
• PhD in a quantitative field plus 1 year of analytics experience

Responsibilities

• Partner with cross-functional teams to deliver data science products with real client value.
• Collaborate with enterprise AI teams to build ML models through all development phases.
• Conduct rigorous statistical analysis and back-testing with third-party data.
• Modernize data infrastructure with tailored analytical tools on core enterprise schemas.
• Support long-term technical integrations for complex business projects.

Skills

Python

Scala

R

SQL

AWS

Machine Learning

Open-source languages

Education

Bachelor's degree in a quantitative field

Master's degree in a quantitative field or MBA with quantitative concentration

PhD in a quantitative field

Tools

Relational databases

Cloud computing platforms

Job description

Capital One is seeking a Data Scientist Manager to lead cross-functional teams across Capital One Data Insights and Analytics and to advance machine learning initiatives across billions of records. You will partner with data scientists, analysts, and relationship managers to deliver data-driven products and insights.

The role emphasizes building ML models through all stages, validating findings, and integrating new datasets, with a strong focus on scalable data infrastructure and enterprise data